Abstract
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.
Highlights
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging
A two-dimensional image is transformed into a thin plate with the same size as the image and a thickness that varies with position according to the grayscale value of the pixel at that given location in the image
In this study, we view image segmentation from a unique perspective, and propose the crack propagation method (CPM), which applies the principles of fracture mechanics to image segmentation
Summary
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. We present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. Using the principles of fracture mechanics, we can obtain the boundary coordinates of the target area in an image by tracing the cracks along the edge of the relevant region in a thin-plate model. This paper establishes this unique method, and demonstrates its great advantage and potential in image segmentation by segmenting synthetic and realistic medical images, especially those including soft tissues with blurred and broken boundaries
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